Search Results for author:
Found 18 papers, 6 papers with code
X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization
Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recognition.
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
In the grasping stage, the model only generates hand motions, whereas in the interaction phase both hand and object poses are synthesized.
FoundPose: Unseen Object Pose Estimation with Foundation Features
Pose hypotheses are then generated from 2D-3D correspondences established by matching DINOv2 patch features between the query image and a retrieved template, and finally optimized by featuremetric refinement.
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community.
Context-Aware Sequence Alignment using 4D Skeletal Augmentation
Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality.
Learning to Align Sequential Actions in the Wild
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
Reconstructing and grounding narrated instructional videos in 3D
Contrary to the standard scenario of instance-level 3D reconstruction, where identical objects or scenes are present in all views, objects in different instructional videos may have large appearance variations given varying conditions and versions of the same product.
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition
To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition.
HoloLens 2 Research Mode as a Tool for Computer Vision Research
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction
Modeling hand-object manipulations is essential for understanding how humans interact with their environment.
Ranked #9 on
hand-object pose
on HO-3D
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding.
H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions
Given a single RGB image, our model jointly estimates the 3D hand and object poses, models their interactions, and recognizes the object and action classes with a single feed-forward pass through a neural network.
Real-Time Seamless Single Shot 6D Object Pose Prediction
For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing.
Ranked #1 on
6D Pose Estimation using RGB
on OCCLUSION
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Most recent approaches to monocular 3D human pose estimation rely on Deep Learning.
Ranked #277 on
3D Human Pose Estimation
on Human3.6M
Structured Prediction of 3D Human Pose with Deep Neural Networks
Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
Ranked #313 on
3D Human Pose Estimation
on Human3.6M
Direct Prediction of 3D Body Poses from Motion Compensated Sequences
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
Predicting People's 3D Poses from Short Sequences
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
Predicting people’s 3D poses from short sequences
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
Ranked #314 on
3D Human Pose Estimation
on Human3.6M
